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    Experimental and ANN-based mechanical analysis of jute fiber-reinforced rHDPE sustainable composites
    (Taylor & Francis Ltd, 2026) Syduzzaman, Md.; Ahmed, A. T. M. Faiz; Rahman, Md. Redowanur; Sezer, Gamze Okyay; Eadul, Fazlul Haque; Hassan, Tanvir; Islam, Md. Jahirul
    High-density polyethylene (HDPE) waste poses a severe environmental threat, with only 9% recycled globally. This study develops sustainable composites by reinforcing recycled HDPE (rHDPE) with RUCOSTAR EEE6-treated jute fabric at 6 and 12 wt.% loadings. Two treatment concentrations (50 g/L and 70 g/L) were applied via dip-pad-dry-cure to enhance hydrophobicity and interfacial adhesion. Six composite variants were fabricated through compression molding, and their physical, mechanical, and hygroscopic properties were evaluated. The 70 g/L treatment significantly reduced water absorption (E70F12 < 3% vs. 9% for untreated RF12), while increasing density confirmed improved impregnation and reduced void content. Tensile tests revealed enhanced ductility (up to 34% higher strain at break) with modest strength trade-offs at 12 wt.% loading. A feed-forward artificial neural network (ANN) was developed to predict the stress-strain constitutive relationship sigma = f(epsilon), with hyperparameters selected via 5-fold cross-validation and stability assessed over ten reruns. The optimized model achieved median R >= 0.97 and MAE below 0.50 MPa on the held-out test set, outperforming polynomial fitting, SVR, and random forest. This hybrid experimental - computational approach demonstrates that EEE6-treated jute/rHDPE composites offer moisture-resistant, high-ductility green materials for structural applications.